Nolte Guido, Meinecke Frank C, Ziehe Andreas, Müller Klaus-Robert
Fraunhofer FIRST.IDA, Kekuléstrasse 7, D-12489 Berlin, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 May;73(5 Pt 1):051913. doi: 10.1103/PhysRevE.73.051913. Epub 2006 May 23.
We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross-correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data.
我们提出了一种技术，该技术可从多通道数据中识别复杂系统中真正相互作用的子系统，前提是这些记录是真实源的未知线性且瞬时混合。该方法对任意噪声结构均有效。为此，我们提出了一种盲源分离技术，该技术可将反对称化的互相关矩阵或互谱矩阵对角化。由此得到的分解能够盲目地找到真正相互作用的子系统，并抑制由混合产生的任何虚假相互作用。这种相互作用源分析的实用性在模拟和真实脑电图数据中得到了证明。
